Lubans et al. International Journal of Behavioral Nutrition and Physical Activity (2016) 13:92 DOI 10.1186/s12966-016-0420-8
RESEARCH
Open Access
Assessing the sustained impact of a schoolbased obesity prevention program for adolescent boys: the ATLAS cluster randomized controlled trial David R. Lubans1*, Jordan J. Smith1, Ronald C. Plotnikoff1, Kerry A. Dally1, Anthony D. Okely2, Jo Salmon3 and Philip J. Morgan1
Abstract Background: Obesity prevention interventions targeting ‘at-risk’ adolescents are urgently needed. The aim of this study is to evaluate the sustained impact of the ‘Active Teen Leaders Avoiding Screen-time’ (ATLAS) obesity prevention program. Methods: Cluster RCT in 14 secondary schools in low-income communities of New South Wales, Australia. Participants were 361 adolescent boys (aged 12–14 years) ‘at risk’ of obesity. The intervention was based on Self-Determination Theory and Social Cognitive Theory and involved: professional development, fitness equipment for schools, teacher-delivered physical activity sessions, lunch-time activity sessions, researcher-led seminars, a smartphone application, and parental strategies. Assessments for the primary (body mass index [BMI], waist circumference) and secondary outcomes were conducted at baseline, 8- (post-intervention) and 18-months (follow-up). Analyses followed the intention-to-treat principle using linear mixed models. Results: After 18-months, there were no intervention effects for BMI or waist circumference. Sustained effects were found for screen-time, resistance training skill competency, and motivational regulations for school sport. Conclusions: There were no clinically meaningful intervention effects for the adiposity outcomes. However, the intervention resulted in sustained effects for secondary outcomes. Interventions that more intensively target the home environment, as well as other socio-ecological determinants of obesity may be needed to prevent unhealthy weight gain in adolescents from low-income communities. Trial registration: Australian Clinical Trial Registry ACTRN12612000978864. Keywords: Intervention, Fitness, Resistance training, Behavior, Disadvantaged Abbreviations: ASAQ, Adolescent sedentary activity questionnaire; ATLAS, Active teen leaders avoiding screentime; BMI, Body mass index; CI, Confidence intervals; CONSORT, Consolidated standards for reporting trials; CPM, Counts per minute; DEXA, Dual-energy x-ray absorptiometry; FMS, Fundamental movement skills; ICC, Intra-class correlation coefficient; IRSD, Index of relative socio-economic disadvantage; MVPA, Moderate to vigorous physical activity; NSW, New South Wales; RCT, Randomized controlled trial; RTSB, Resistance training skills battery; SDT, Self-determination theory; SEIFA, Socio-economic indexes for areas; SEP, Socio-economic position; SPANS, Schools physical activity and nutrition survey; SSB, Sugar-sweetened beverages
* Correspondence: [email protected] 1 Priority Research Centre in Physical Activity and Nutrition, School of Education, University of Newcastle, Callaghan, NSW, Australia Full list of author information is available at the end of the article © 2016 The Author(s). Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Lubans et al. International Journal of Behavioral Nutrition and Physical Activity (2016) 13:92
Background Obesity is a global public health challenge and in developed countries 24 % of boys and 23 % of girls were overweight or obese in 2013 [1]. Although there is some evidence suggesting rates of pediatric obesity have levelled off in developed nations [2], this trend has not been observed in low-income communities [3]. Youth of low socio-economic position (SEP) have poorer nutritional knowledge [4] and receive less family support for physical activity and healthy eating compared with young people from middle and high socio-economic strata [4]. In addition, low-income youth are less active and spend more time engaged in sedentary screen-based recreation, compared with their higher-income peers [5]. Although the etiology of obesity is complex, these factors may partially explain why rates of obesity among lowincome youth are continuing to rise. Schools represent an ideal setting to address social inequalities because they provide access to the population and generally have the necessary facilities, curriculum, environment and personnel to promote physical activity and healthy eating [6]. School-based obesity prevention interventions targeting adolescents are particularly important as physical activity declines [7], dietary behaviors deteriorate [8] and recreational screen-time increases [9] during the teenage years. Preventing unhealthy weight gain in adolescent populations is challenging and evidence from the most recent Cochrane review of schoolbased obesity prevention interventions suggests that intervention effects among adolescents have been minimal (i.e., mean standardized difference in BMI/ BMI z-score = −.09 units; 95 % Confidence Intervals [CI] = −.20 to .03). In addition, little is known regarding the sustainability of intervention effects [10, 11], as few obesity prevention programs have assessed the maintenance of improvements in adiposity and health behaviors beyond immediate posttest assessments [10]. There is clearly a need for innovative interventions that target adolescents who are ‘at-risk’ of obesity and assess the maintenance of effects over time. Previous school-based interventions have reported differential effects for boys and girls [12, 13], suggesting that males and females might benefit from more targeted intervention approaches. For example, boys may be more receptive than girls to participation in more traditionally masculine activities such as resistance training. Although muscle-strengthening activities are recommended for both boys and girls [14, 15], it is important that these physical activity preferences are recognized, particularly for interventions attempting to engage otherwise inactive youth. There are also clear and consistent gender differences in key weight-related behaviors. For example, despite being more active, boys are more likely than girls to consume unhealthy quantities of sugar-sweetened
Page 2 of 12
beverages (SSB) and engage in high levels of recreational screen-time [16]. These unique socio-cultural differences should inform the design and delivery of health behavior interventions for youth. Active Teen Leaders Avoiding Screen-time (ATLAS) was an obesity prevention program targeting disadvantaged adolescent boys, considered ‘at-risk’ of obesity based on their physical activity and recreational screentime behaviors. ATLAS was designed to be culturally appropriate and incorporated mHealth (i.e., mobile phone) technology to supplement the school- and home-based components. We previously reported null findings for changes in adiposity, but significant group-by-time interaction effects for recreational screen-time (−30 mins/d, p = .03), SSB intake (−0.6 glass/d, p = .01), muscular fitness (0.9 repetitions, p = .04) and resistance training skill competency (5.7 units, p < .001) [17] and a small positive effect for psychological well-being [18]. The aim of this paper is to report the sustained impact of the ATLAS program on primary and secondary outcomes which were assessed 10-months after program completion (i.e., 18-months post baseline).
Methods Study design, setting and participants
Ethics approval for this study was obtained from the University of Newcastle, Australia and the New South Wales (NSW) Department of Education and Communities. School principals, teachers, parents and study participants all provided informed written consent. The design, conduct and reporting of this trial adheres to the CONSORT statement (see Additional file 1). The rationale and study protocol has been described in detail previously [19]. Briefly, ATLAS was evaluated using a cluster RCT conducted in state-funded secondary schools within lowincome areas of NSW, Australia. The Socio Economic Indexes For Areas (SEIFA) Index of Relative Socioeconomic Disadvantage (IRSD) (scale, 1 = lowest to 10 = highest) was used to identify eligible schools. Schools located in the Newcastle, Hunter and Central Coast regions of NSW classified within an IRSD decile ≤ 5 (lowest 50 %) were considered eligible. All male students in their first year at the study schools completed a short screening questionnaire to assess their eligibility for inclusion. Students failing to meet either international physical activity ( +1SD, Obesity: > +2SD for age and sexadjusted BMI z-scores Abbreviations: BMI body mass index; SEP socio-economic position a One participant did not report language spoken at home b Two participants did not report cultural background c Socioeconomic position determined by population decile using SocioEconomic Indexes For Areas Index of Relative Socioeconomic Disadvantage based on residential postcode (1 = lowest, 10 = highest). Two participants did not report residential postcode
resistance training skill competency (mean = 5.9 units, 95 % CI = 4.5 to 7.3, p < .001).
Changes in motivation for school sport
There were group-by-time interaction effects for intrinsic, identified, introjected and external regulations in favor of the intervention group (range for adjusted difference between groups = .40 to .56, p < .05 for all). There was no intervention effect for amotivation for school sport, which increased for both groups over the study period.
Page 6 of 12
Discussion The aim of this paper was to report the sustained impact of the ATLAS intervention on adiposity, fitness and health behaviors in a sample of adolescent boys attending schools in low-income communities. Contrary to our primary hypothesis, the ATLAS intervention had no immediate or sustained impact on adiposity. There was some support for a positive effect among participants who were overweight or obese at baseline, as demonstrated by a reduction in BMI z-score within the intervention group. However, the effect is unlikely to be clinically meaningful and may reflect fluctuations in adiposity not attributable to the intervention. Our null findings are consistent with previous obesity prevention interventions targeting adolescents, which have been less successful than interventions targeting children [10]. Such findings may highlight the need for earlier intervention to prevent obesity among youth. ATLAS was an obesity ‘prevention’ program and participants were considered to be ‘at-risk’, based on their physical activity and screen-time behaviors. Although the inclusion of BMI and waist circumference criteria may have increased our ability to identify and target overweight and obese adolescents, this approach was considered unacceptable by the NSW Department of Education, due to concerns that participants may experience stigmatization. Consequently, only 35 % of the study sample was overweight or obese, thus limiting our capacity to detect meaningful changes in adiposity. While our null findings for the primary outcomes are disappointing, we were successful in recruiting and retaining more than 100 overweight/obese adolescent boys, who benefited from participating in the program in other ways (e.g., improvements in screen-time, movement skills, motivation etc). Of note, community-based obesity treatment programs often report difficulty recruiting and retaining overweight youth [49, 50]. Part of the challenge in evaluating obesity prevention interventions is the selection of the most appropriate outcome measure. Although BMI is considered a good measure of adiposity change in growing children [51], it may lack sensitivity in intervention trials conducted with adolescents. Peak height velocity typically occurs during earlyto-mid adolescence and it is likely that such drastic changes in height and weight masks intervention effects. A similar intervention study with adolescent boys recently reported improvements in multiple adiposity variables measured using Dual-Energy X-ray Absorptiometry (DEXA) following 8 weeks of resistance and interval training [52]. Unlike BMI, which does not distinguish between mass from different body tissues, DEXA allows researchers to determine changes specifically in fat mass. Considering weight-bearing exercise can increase adolescents’ muscle and bone mass [53], it is possible that
Lubans et al. International Journal of Behavioral Nutrition and Physical Activity (2016) 13:92
Page 7 of 12
Table 2 Changes in primary and secondary outcomes for the intervention and control groups Outcomea
Baseline, Mean (SE)
n
8-month, Mean (SE)
n
Timeb p
18-month, Mean (SE)
n
Timec p
Adjusted difference in change, Mean (95 % CI)d
Group-by-timed p
Intervention
20.8 (.6)
181
21.4 (.7)
139
RESEARCH
Open Access
Assessing the sustained impact of a schoolbased obesity prevention program for adolescent boys: the ATLAS cluster randomized controlled trial David R. Lubans1*, Jordan J. Smith1, Ronald C. Plotnikoff1, Kerry A. Dally1, Anthony D. Okely2, Jo Salmon3 and Philip J. Morgan1
Abstract Background: Obesity prevention interventions targeting ‘at-risk’ adolescents are urgently needed. The aim of this study is to evaluate the sustained impact of the ‘Active Teen Leaders Avoiding Screen-time’ (ATLAS) obesity prevention program. Methods: Cluster RCT in 14 secondary schools in low-income communities of New South Wales, Australia. Participants were 361 adolescent boys (aged 12–14 years) ‘at risk’ of obesity. The intervention was based on Self-Determination Theory and Social Cognitive Theory and involved: professional development, fitness equipment for schools, teacher-delivered physical activity sessions, lunch-time activity sessions, researcher-led seminars, a smartphone application, and parental strategies. Assessments for the primary (body mass index [BMI], waist circumference) and secondary outcomes were conducted at baseline, 8- (post-intervention) and 18-months (follow-up). Analyses followed the intention-to-treat principle using linear mixed models. Results: After 18-months, there were no intervention effects for BMI or waist circumference. Sustained effects were found for screen-time, resistance training skill competency, and motivational regulations for school sport. Conclusions: There were no clinically meaningful intervention effects for the adiposity outcomes. However, the intervention resulted in sustained effects for secondary outcomes. Interventions that more intensively target the home environment, as well as other socio-ecological determinants of obesity may be needed to prevent unhealthy weight gain in adolescents from low-income communities. Trial registration: Australian Clinical Trial Registry ACTRN12612000978864. Keywords: Intervention, Fitness, Resistance training, Behavior, Disadvantaged Abbreviations: ASAQ, Adolescent sedentary activity questionnaire; ATLAS, Active teen leaders avoiding screentime; BMI, Body mass index; CI, Confidence intervals; CONSORT, Consolidated standards for reporting trials; CPM, Counts per minute; DEXA, Dual-energy x-ray absorptiometry; FMS, Fundamental movement skills; ICC, Intra-class correlation coefficient; IRSD, Index of relative socio-economic disadvantage; MVPA, Moderate to vigorous physical activity; NSW, New South Wales; RCT, Randomized controlled trial; RTSB, Resistance training skills battery; SDT, Self-determination theory; SEIFA, Socio-economic indexes for areas; SEP, Socio-economic position; SPANS, Schools physical activity and nutrition survey; SSB, Sugar-sweetened beverages
* Correspondence: [email protected] 1 Priority Research Centre in Physical Activity and Nutrition, School of Education, University of Newcastle, Callaghan, NSW, Australia Full list of author information is available at the end of the article © 2016 The Author(s). Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Lubans et al. International Journal of Behavioral Nutrition and Physical Activity (2016) 13:92
Background Obesity is a global public health challenge and in developed countries 24 % of boys and 23 % of girls were overweight or obese in 2013 [1]. Although there is some evidence suggesting rates of pediatric obesity have levelled off in developed nations [2], this trend has not been observed in low-income communities [3]. Youth of low socio-economic position (SEP) have poorer nutritional knowledge [4] and receive less family support for physical activity and healthy eating compared with young people from middle and high socio-economic strata [4]. In addition, low-income youth are less active and spend more time engaged in sedentary screen-based recreation, compared with their higher-income peers [5]. Although the etiology of obesity is complex, these factors may partially explain why rates of obesity among lowincome youth are continuing to rise. Schools represent an ideal setting to address social inequalities because they provide access to the population and generally have the necessary facilities, curriculum, environment and personnel to promote physical activity and healthy eating [6]. School-based obesity prevention interventions targeting adolescents are particularly important as physical activity declines [7], dietary behaviors deteriorate [8] and recreational screen-time increases [9] during the teenage years. Preventing unhealthy weight gain in adolescent populations is challenging and evidence from the most recent Cochrane review of schoolbased obesity prevention interventions suggests that intervention effects among adolescents have been minimal (i.e., mean standardized difference in BMI/ BMI z-score = −.09 units; 95 % Confidence Intervals [CI] = −.20 to .03). In addition, little is known regarding the sustainability of intervention effects [10, 11], as few obesity prevention programs have assessed the maintenance of improvements in adiposity and health behaviors beyond immediate posttest assessments [10]. There is clearly a need for innovative interventions that target adolescents who are ‘at-risk’ of obesity and assess the maintenance of effects over time. Previous school-based interventions have reported differential effects for boys and girls [12, 13], suggesting that males and females might benefit from more targeted intervention approaches. For example, boys may be more receptive than girls to participation in more traditionally masculine activities such as resistance training. Although muscle-strengthening activities are recommended for both boys and girls [14, 15], it is important that these physical activity preferences are recognized, particularly for interventions attempting to engage otherwise inactive youth. There are also clear and consistent gender differences in key weight-related behaviors. For example, despite being more active, boys are more likely than girls to consume unhealthy quantities of sugar-sweetened
Page 2 of 12
beverages (SSB) and engage in high levels of recreational screen-time [16]. These unique socio-cultural differences should inform the design and delivery of health behavior interventions for youth. Active Teen Leaders Avoiding Screen-time (ATLAS) was an obesity prevention program targeting disadvantaged adolescent boys, considered ‘at-risk’ of obesity based on their physical activity and recreational screentime behaviors. ATLAS was designed to be culturally appropriate and incorporated mHealth (i.e., mobile phone) technology to supplement the school- and home-based components. We previously reported null findings for changes in adiposity, but significant group-by-time interaction effects for recreational screen-time (−30 mins/d, p = .03), SSB intake (−0.6 glass/d, p = .01), muscular fitness (0.9 repetitions, p = .04) and resistance training skill competency (5.7 units, p < .001) [17] and a small positive effect for psychological well-being [18]. The aim of this paper is to report the sustained impact of the ATLAS program on primary and secondary outcomes which were assessed 10-months after program completion (i.e., 18-months post baseline).
Methods Study design, setting and participants
Ethics approval for this study was obtained from the University of Newcastle, Australia and the New South Wales (NSW) Department of Education and Communities. School principals, teachers, parents and study participants all provided informed written consent. The design, conduct and reporting of this trial adheres to the CONSORT statement (see Additional file 1). The rationale and study protocol has been described in detail previously [19]. Briefly, ATLAS was evaluated using a cluster RCT conducted in state-funded secondary schools within lowincome areas of NSW, Australia. The Socio Economic Indexes For Areas (SEIFA) Index of Relative Socioeconomic Disadvantage (IRSD) (scale, 1 = lowest to 10 = highest) was used to identify eligible schools. Schools located in the Newcastle, Hunter and Central Coast regions of NSW classified within an IRSD decile ≤ 5 (lowest 50 %) were considered eligible. All male students in their first year at the study schools completed a short screening questionnaire to assess their eligibility for inclusion. Students failing to meet either international physical activity ( +1SD, Obesity: > +2SD for age and sexadjusted BMI z-scores Abbreviations: BMI body mass index; SEP socio-economic position a One participant did not report language spoken at home b Two participants did not report cultural background c Socioeconomic position determined by population decile using SocioEconomic Indexes For Areas Index of Relative Socioeconomic Disadvantage based on residential postcode (1 = lowest, 10 = highest). Two participants did not report residential postcode
resistance training skill competency (mean = 5.9 units, 95 % CI = 4.5 to 7.3, p < .001).
Changes in motivation for school sport
There were group-by-time interaction effects for intrinsic, identified, introjected and external regulations in favor of the intervention group (range for adjusted difference between groups = .40 to .56, p < .05 for all). There was no intervention effect for amotivation for school sport, which increased for both groups over the study period.
Page 6 of 12
Discussion The aim of this paper was to report the sustained impact of the ATLAS intervention on adiposity, fitness and health behaviors in a sample of adolescent boys attending schools in low-income communities. Contrary to our primary hypothesis, the ATLAS intervention had no immediate or sustained impact on adiposity. There was some support for a positive effect among participants who were overweight or obese at baseline, as demonstrated by a reduction in BMI z-score within the intervention group. However, the effect is unlikely to be clinically meaningful and may reflect fluctuations in adiposity not attributable to the intervention. Our null findings are consistent with previous obesity prevention interventions targeting adolescents, which have been less successful than interventions targeting children [10]. Such findings may highlight the need for earlier intervention to prevent obesity among youth. ATLAS was an obesity ‘prevention’ program and participants were considered to be ‘at-risk’, based on their physical activity and screen-time behaviors. Although the inclusion of BMI and waist circumference criteria may have increased our ability to identify and target overweight and obese adolescents, this approach was considered unacceptable by the NSW Department of Education, due to concerns that participants may experience stigmatization. Consequently, only 35 % of the study sample was overweight or obese, thus limiting our capacity to detect meaningful changes in adiposity. While our null findings for the primary outcomes are disappointing, we were successful in recruiting and retaining more than 100 overweight/obese adolescent boys, who benefited from participating in the program in other ways (e.g., improvements in screen-time, movement skills, motivation etc). Of note, community-based obesity treatment programs often report difficulty recruiting and retaining overweight youth [49, 50]. Part of the challenge in evaluating obesity prevention interventions is the selection of the most appropriate outcome measure. Although BMI is considered a good measure of adiposity change in growing children [51], it may lack sensitivity in intervention trials conducted with adolescents. Peak height velocity typically occurs during earlyto-mid adolescence and it is likely that such drastic changes in height and weight masks intervention effects. A similar intervention study with adolescent boys recently reported improvements in multiple adiposity variables measured using Dual-Energy X-ray Absorptiometry (DEXA) following 8 weeks of resistance and interval training [52]. Unlike BMI, which does not distinguish between mass from different body tissues, DEXA allows researchers to determine changes specifically in fat mass. Considering weight-bearing exercise can increase adolescents’ muscle and bone mass [53], it is possible that
Lubans et al. International Journal of Behavioral Nutrition and Physical Activity (2016) 13:92
Page 7 of 12
Table 2 Changes in primary and secondary outcomes for the intervention and control groups Outcomea
Baseline, Mean (SE)
n
8-month, Mean (SE)
n
Timeb p
18-month, Mean (SE)
n
Timec p
Adjusted difference in change, Mean (95 % CI)d
Group-by-timed p
Intervention
20.8 (.6)
181
21.4 (.7)
139
